Surface normal estimation \(\mathcal {F}_{SN}\)  estimates surface normal \(\mathcal {S}_{\mathcal {B}}\)  from RGB image \(\mathcal {I}_{\mathcal {B}}\) . Although previous category-level pose estimation works [1]}, [2]} show that depth is enough to obtain opaque objects' pose, experiments in Section REF  demonstrate that surface normal is not a redundant input for transparent object pose estimation. Here, we slightly modify U-Net [3]} to perform the surface normal estimation.
\(\hat{\mathcal {S}}_\mathcal {B} = \mathcal {F}_{SN}\left(\mathcal {I}_\mathcal {B}\right)\) 
